Using a virtual
driver system, driverless automobiles and trucks, also known as self-driving or
autonomous vehicles, are capable of moving through settings with little or no
human control.
A virtual driver system is a set of characteristics and
capabilities that augment or replicate the actions of an absent driver to the
point that, at the maximum degree of autonomy, the driver may not even be
present.
Diverse technology uses, restricting circumstances, and
categorization methods make reaching an agreement on what defines a driverless
car difficult.
A semiautonomous system, in general, is one in which the
human performs certain driving functions (such as lane maintaining) while
others are performed autonomously (such as acceleration and deceleration).
All driving activities are autonomous only under certain
circumstances in a conditionally autonomous system.
All driving duties are automated in a fully autonomous
system.
Automobile manufacturers, technology businesses, automotive
suppliers, and universities are all testing and developing applications.
Each builder's car or system, as well as the technical road
that led to it, demonstrates a diverse range of technological answers to the
challenge of developing a virtual driving system.
Ambiguities exist at the level of defining circumstances, so
that a same technological system may be characterized in a variety of ways
depending on factors such as location, speed, weather, traffic density, human
attention, and infrastructure.
When individual driving duties are operationalized for
feature development and context plays a role in developing solutions, more
complexity is generated (such as connected vehicles, smart cities, and
regulatory environment).
Because of this complication, producing driverless cars
often necessitates collaboration across several roles and disciplines of study,
such as hardware and software engineering, ergonomics, user experience, legal
and regulatory, city planning, and ethics.
The development of self-driving automobiles is both a
technical and a socio-cultural enterprise.
The distribution of mobility tasks across an array of
equipment to collectively perform a variety of activities such as assessing
driver intent, sensing the environment, distinguishing objects, mapping and
wayfinding, and safety management are among the technical problems of
engineering a virtual driver system.
LIDAR, radar, computer vision, global positioning, odometry,
and sonar are among the hardware and software components of a virtual driving
system.
There are many approaches to solving discrete autonomous movement
problems.
With cameras, maps, and sensors, sensing and processing can
be centralized in the vehicle, or it can be distributed throughout the
environment and across other vehicles, as with intelligent infrastructure and
V2X (vehicle to everything) capability.
The burden and scope of this processing—and the scale of the
problems to be solved—are closely related to the expected level of human
attention and intervention, and as a result, the most frequently referenced
classification of driverless capability is formally structured along the lines
of human attentional demands and intervention requirements by the Society of
Automotive Engineers, and has been adopted in 2 years.
These companies use various levels of driver automation,
ranging from Level 0 to Level 5.
Level 0 refers to no automation, which means the human
driver is solely responsible for longitudinal and latitudinal control
(steering) (acceleration and deceleration).
On Level 0, the human driver is in charge of keeping an eye
on the environment and reacting to any unexpected safety hazards.
Automated systems that take control of longitudinal or
latitudinal control are classified as Level 1, or driver aid.
The driver is in charge of observation and intervention.
Level 2 denotes partial automation, in which the virtual
driver system is in charge of both lateral and longitudinal control.
The human driver is deemed to be in the loop, which means
that they are in charge of monitoring the environment and acting in the event
of a safety-related emergency.
Level 2 capability has not yet been achieved by commercially
available systems.
The monitoring capabilities of the virtual driving system
distinguishes Level 3 conditional autonomy from Level 2.
At this stage, the human driver may be disconnected from the
surroundings and depend on the autonomous system to keep track of it.
The person is required to react to calls for assistance in a
range of situations, such as during severe weather or in construction zones.
A navigation system (e.g., GPS) is not required at this
level.
To operate at Level 2 or Level 3, a vehicle does not need a
map or a specific destination.
A human driver is not needed to react to a request for
intervention at Level 4, often known as high automation.
The virtual driving system is in charge of navigation,
locomotion, and monitoring.
When a specific condition cannot be satisfied, such as when
a navigation destination is obstructed, it may request that a driver intervene.
If the human driver does not choose to interfere, the system
may safely stop or redirect based on the engineering approach.
The classification of this situation is based on standards
of safe driving, which are established not only by technical competence and
environmental circumstances, but also by legal and regulatory agreements and
lawsuit tolerance.
Level 5 autonomy, often known as complete automation, refers
to a vehicle that is capable of doing all driving activities in any situation
that a human driver could handle.
Although Level 4 and Level 5 systems do not need the
presence of a person, they still necessitate substantial technological and
social cooperation.
While efforts to construct autonomous vehicles date back to
the 1920s, Leonardo Da Vinci is credited with the concept of a self-propelled
cart.
In his 1939 New York World's Fair Futurama display, Norman
Bel Geddes envisaged a smart metropolis of the future inhabited by self-driving
automobiles.
Automobiles, according to Bel Geddes, will be outfitted with
"technology that would rectify the mistakes of human drivers" by
1960.
General Motors popularized the concept of smart
infrastructure in the 1950s by building a "automated highway" with
steering-assist circuits.
In 1960, the business tested a working prototype car, but
owing to the expensive expense of infrastructure, it quickly moved its focus
from smart cities to smart autos.
A team lead by Sadayuki Tsugawa of Tsukuba Mechanical
Engineering Laboratory in Japan created an early prototype of an autonomous
car.
Their 1977 vehicle operated under predefined environmental
conditions dictated by lateral guiding rails.
The truck used cameras to track the rails, and most of the
processing equipment was aboard.
The EUREKA (European Research Organization) pooled money and
experience in the 1980s to enhance the state-of-the-art in cameras and
processing for autonomous cars.
Simultaneously, Carnegie Mellon University in Pittsburgh,
Pennsylvania pooled their resources for research on autonomous navigation
utilizing GPS data.
Since then, automakers including General Motors, Tesla, and
Ford Motor Company, as well as technology firms like ARGO AI and Waymo, have
been working on autonomous cars or critical components.
The technology is becoming less dependent on very limited
circumstances and more adaptable to real-world scenarios.
Manufacturers are currently producing Level 4 autonomous
test cars, and testing are being undertaken in real-world traffic and weather
situations.
Commercially accessible Level 4 self-driving cars are still
a long way off.
There are supporters and opponents of autonomous driving.
Supporters point to a number of benefits that address social
problems, environmental concerns, efficiency, and safety.
The provision of mobility services and a degree of autonomy
to those who do not already have access, such as those with disabilities (e.g.,
blindness or motor function impairment) or those who are unable to drive, such
as the elderly and children, is one such social benefit.
The capacity to decrease fuel economy by managing
acceleration and braking has environmental benefits.
Because networked cars may go bumper to bumper and are
routed according to traffic optimization algorithms, congestion is expected to
be reduced.
Finally, self-driving vehicles have the potential to be
safer.
They may be able to handle complicated information more quickly
and thoroughly than human drivers, resulting in fewer collisions.
Self-driving car negative repercussions may be included in
any of these areas.
In terms of society, driverless cars may limit access to
mobility and municipal services.
Autonomous mobility may be heavily regulated, costly, or
limited to places that are inaccessible to low-income commuters.
Non-networked or manually operated cars might be kept out of
intelligent geo-fenced municipal infrastructure.
Furthermore, if no adult or responsible human party is
present during transportation, autonomous automobiles may pose a safety concern
for some susceptible passengers, such as children.
Greater convenience may have environmental consequences.
Drivers may sleep or work while driving autonomously, which
may have the unintended consequence of extending commutes and worsening traffic
congestion.
Another security issue is widespread vehicle hacking, which
could bring individual automobiles and trucks, or even a whole city, to a halt.
~ Jai Krishna Ponnappan
You may also want to read more about Artificial Intelligence here.
See also:
Accidents and Risk Assessment; Autonomous and Semiautonomous Systems; Autonomy and Complacency; Intelligent Transportation; Trolley Problem.
Further Reading:
Antsaklis, Panos J., Kevin M. Passino, and Shyh J. Wang. 1991. “An Introduction to Autonomous Control Systems.” IEEE Control Systems Magazine 11, no. 4: 5–13.
Bel Geddes, Norman. 1940. Magic Motorways. New York: Random House.
Bimbraw, Keshav. 2015. “Autonomous Cars: Past, Present, and Future—A Review of the Developments in the Last Century, the Present Scenario, and the Expected Future of Autonomous Vehicle Technology.” In ICINCO: 2015—12th International Conference on Informatics in Control, Automation and Robotics, vol. 1, 191–98. Piscataway, NJ: IEEE.
Kröger, Fabian. 2016. “Automated Driving in Its Social, Historical and Cultural Contexts.” In Autonomous Driving, edited by Markus Maurer, J. Christian Gerdes, Barbara Lenz, and Hermann Winner, 41–68. Berlin: Springer.
Lin, Patrick. 2016. “Why Ethics Matters for Autonomous Cars.” In Autonomous Driving, edited by Markus Maurer, J. Christian Gerdes, Barbara Lenz, and Hermann Winner, 69–85. Berlin: Springer.
Weber, Marc. 2014. “Where To? A History of Autonomous Vehicles.” Computer History Museum. https://www.computerhistory.org/atchm/where-to-a-history-of-autonomous-vehicles/.
